Peer Revenue Impact factor: volatile

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

All of arXiv

Thursday 24 September

Astrophysics of Galaxies

The geometric origin of the thermal eccentricity law

Václav Pavlík

The distribution $f(e)=2e$ is commonly referred to as the `thermal eccentricity distribution' of Keplerian binaries. However, the result does not require thermal equilibrium, and its usual derivations obscure why the distribution is linear in eccentricity. We seek a simple geometrical interpretation of this result and its relation to more general eccentricity distributions. We consider bound Kepler orbits in a Euclidean space of dimension $D\geq2$, with a phase-space distribution depending only on energy, and derive the eccentricity distribution from the Liouville measure. We obtain $f_D(e)=(D-1) e (1-e^2)^{(D-3)/2}$, so that, within this family, $D=3$ is the only case for which the distribution function is linear in $e$. This follows from the $D-1$ transverse momentum dimensions and the Kepler period degeneracy. We further show that two known generalisations – namely a power-law weighting of the normalised angular momentum and a constant velocity anisotropy – are, in fact, two representations of the same one-parameter family of eccentricity distributions.
Read the full abstract

Machine Learning

Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning

Sudip Bhujel, Shanghao Shi, Ruiquan Huang et al. (+2 more)

Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only policy gradients to the server. Yet temporal structure can amplify this leakage beyond single-frame attacks. We introduce Temporal Reconstruction Attack on Consecutive Encodings (TRACE), an amortized temporal gradient-inversion attack that autoregressively reconstructs the sequence of private observation-action trajectories from per-step policy-learning gradients. The attack exploits two structural signals ignored by prior single-frame methods: (i) cross-time correlation between successive embodied gradients, which we formalize via a conditional mutual-information bound, and (ii) closed-form action recovery from policy-head gradient structure, which we prove exact when standard entropy regularization is sufficiently small. On held-out embodied scenes, TRACE reaches $18.8$ dB PSNR with near-perfect action recovery at $3$-$4.5$ ms per reconstructed frame, dominating the learning-based baseline across all reconstruction metrics and exceeding optimization attacks while running orders of magnitude faster. Further evaluation demonstrates TRACE's broader applicability across recurrent, residual, and compact transformer victim architectures, multi-modal inputs, and larger discrete action spaces. Defense experiments suggest that protecting temporal gradient streams may require sequence-aware privacy mechanisms.
Read the full abstract

All authors Sudip Bhujel, Shanghao Shi, Ruiquan Huang, Ning Zhang, Yang Xiao

Combinatorics

A quasisymmetric analog of Grassmannian Schubert varieties

Teddy Gonzales, Tuong Le and Chayim Lowen

We show that the cohomology rings of toric Richardson varieties in the Grassmannian are finite truncations of the ring of quasisymmetric functions. We exhibit an affine paving of each such variety whose cell closures give rise to the basis of fundamental quasisymmetric functions. We similarly interpret the homology of these varieties in terms of the ring of noncommutative symmetric functions and show that the expansion of the homological class of any torus-invariant subvariety into the affine paving basis agrees with the expansion of a corresponding generalized noncommutative ribbon function into the ribbon basis. By taking the direct limit of all toric Richardson varieties, we obtain an ind-variety equipped with a weak $H$-group structure whose cohomology is the Hopf algebra of quasisymmetric functions. We conjecture that it is isomorphic to a similar $H$-group constructed by Baker–Richter. As a byproduct, we deduce that the $f$-vectors of shard polytopes are log-concave, making the first progress on a question of Ferroni–Schröter for matroid base polytopes.
Read the full abstract

Quantum Physics

Quantum Feature Selection for Biomedical Data Analysis

Hongbin Liu, Robert Lahmann, Benjamin Campbell et al. (+4 more)

Feature selection is an essential step for reducing complexity of high dimensional data, usually in preparation for developing machine learning models such as computational biomarkers. However, there are limitations associated with feature selection such as for metabolomic data where there are hundreds or even thousands of features per study participant, while the available number of participants in a clinical trials is limited. Classical methods such as exhaustive search require evaluation of all possible feature combinations, making them costly in terms of computation and runtime, or even infeasible, as feature dimensionality increases. In this study, we propose a novel Quadratic Unconstrained Binary Optimization (QUBO) coefficient formulation and pose metabolomic feature selection as a QUBO problem that selects a specified number of features by balancing their relevance against redundancy among the selected variables. To evaluate our proposed QUBO objective function, we conducted a series of experiments using Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO) and Quantum Approximate Optimization Algorithm (QAOA) on a quantum gate based computer. We also compared the method to several classical methods on three metabolomic datasets associated with Autism Spectrum Disorder (ASD) on a classical computer. Our method reduces runtime compared with exhaustive search and Iterative Tabu Search (ITS) and achieves competitive performance across classifiers compared to classical filter, wrapper, and embedded methods. These results do not claim quantum advantage; rather, they establish hardware feasibility and demonstrate the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices.
Read the full abstract

All authors Hongbin Liu, Robert Lahmann, Benjamin Campbell, Zhemin Zhang, Zhiding Liang, Siona Bapat, Juergen Hahn

High Energy Physics (Phenomenology)

A Narrow Neutrino Window for the LZ Event

Vedran Brdar and Dibya S. Chattopadhyay

The LUX-ZEPLIN (LZ) experiment has reported an event consistent with a $248$ keV nuclear recoil. Explaining the absence of lower-energy recoil events typically calls for some form of upscattering that kinematically forbids such events. In this work, we present a framework in which the LZ observation has a neutrino origin, with atmospheric neutrinos providing the dominant flux in the required energy range. A scenario in which atmospheric neutrinos upscatter to a heavier neutral state would also produce a large number of neutral-current events in neutrino experiments. In particular, scattering on lighter nuclear targets results in much larger nuclear recoil energies compared to xenon, yet no such excess has been observed. We show that this constraint from neutrino experiments can be evaded if atmospheric neutrinos within a narrow energy window first produce a nearly monoenergetic state $N_1$, followed by the upscattering of $N_1$ to its heavier partner $N_2$ in LZ. In such a scenario, scattering on xenon becomes kinematically allowed for $N_2$ masses around $250$ MeV, while scattering on oxygen, carbon, and other targets used in large-scale neutrino experiments remains kinematically forbidden. We show that this two-step process, $\nu \to N_1 \to N_2$, can be realized through a parametric resonance induced by a dark matter background that efficiently produces $N_1$, followed by $N_1 \to N_2$ upscattering mediated by a vector boson in a model with gauged $U(1)_B$. The latter interaction can be sufficiently stronger than the weak interaction, which is necessary to lift the neutrino floor and yield $\mathscr{O}(1)$ event at LZ.
Read the full abstract

Programming Languages

Forte: A sensitivity type system for imperative Rust

Chiké Abuah

We introduce Forte, a sensitivity type system for Rust whose soundness rests on ownership. The graded sensitivity type systems, from Fuzz's linear grading to Solo's environment indices, are pure calculi: a claim about a value holds for the value's whole lifetime because nothing can mutate it. The imperative sensitivity analyses admit assignment to first-order variables and no references, so no question of aliasing arises in them. The programs that compute differentially private statistics in deployment are Rust, and they mutate through borrows. Forte closes this gap. Its central rules strongly update a sensitivity environment through an exclusive borrow, at a primitive call and across a checked function boundary; its soundness theorem is metric preservation over an operational semantics with a store, in which the exclusivity of &mut alone licenses framing across a mutating call, and two aliased borrows suffice to refute the theorem without it. Verus mechanizes the theorem, the function rule, and the refutation. Flux checks Forte as an ordinary library, with no fork of the compiler; a machine-checked theorem backs every deterministic primitive signature, and a correspondence theorem transports metric preservation to the programs the checker accepts. We evaluate Forte on mechanism kernels from OpenDP with genuine in-place mutation, matching the library's trusted stability maps with checked constants, covering the constructors that have no proof document, rejecting off-by-one diameters, tightened bounds, miscalibrated releases, and overspent budgets, and deriving one trusted constant as an inferred loop invariant.
Read the full abstract

Quantum Physics

How Not to Build Microcrypt

Aditya Gulati, Dakshita Khurana and Kabir Tomer

A key challenge in quantum cryptography is to build quantum one-wayness and pseudorandomness without the use of (quantum computable) one-way functions. So far, this has turned out to be a difficult task, with only a few proposed candidates that are not directly built from one-way functions. Even within these few proposed candidates, we have lacked the techniques to analyze when proposed constructions may inadvertently yield one-way functions. In this work, we introduce efficient NP-aided shadow tomography of quantum states. We prove that collections of computable pure states can be learned via efficient NP-aided shadow tomography, where we say that a state is computable if the amplitude and phase on any computational basis term can be classically efficiently computed given the description of an efficient preparation circuit for the state. We also give new algorithms for NP-aided learning of unitaries given polynomially many queries to the unitary. By building on this, we show that many existing architectures for PRS and PRU, including some that were explicitly introduced for the purposes of avoiding one-way functions (e.g., Hamiltonian Phase States, Bostanci et. al., TQC 2025), actually do imply the existence of one-way functions or imply \(\NP\) hardness. We hope that these no-go results will inform future investigations into building PRS and PRUs from assumptions that are plausibly outside the complexity class NP.
Read the full abstract

Earth and Planetary Astrophysics

Simulating the thermodynamics of gas, radiation, and multispecies dust: Method and applications to protoplanetary disks

Dhruv Muley, David Melon Fuksman, Prakruti Sudarshan et al. (+2 more)

The thermodynamics of protoplanetary disks, governed by a complex interplay between gas, dust, and radiation, exerts a strong influence on their morphology and dynamics at both large and small scales. Historically, hydrodynamical simulations have treated thermodynamics with approximate prescriptions, such as local isothermality, parametrized $\beta$-cooling, and radiation hydrodynamics with gas-dust thermal equilibrium. However, a more comprehensive and self-consistent approach would more accurately reproduce the wealth of features found in observations, which in modern times have reached unprecedented spectral, angular, and vertical resolution. With this motivation, we have devised a radiation-hydrodynamics scheme for the PLUTO code with energy exchange (via absorption, emission, and collisions) for gas, radiation, and multiple species of dust. Dust-gas dynamics are handled by treating each dust species as a pressureless, diffusive fluid, ensuring that our scheme can represent changes in disk illumination caused by grain settling and trapping. We demonstrate the effectiveness of our scheme in several test problems, and conclude by discussing its relevance to open questions in protoplanetary disk studies.
Read the full abstract

All authors Dhruv Muley, David Melon Fuksman, Prakruti Sudarshan, Alexandros Ziampras, Mario Flock

Cosmology and Nongalactic Astrophysics

Statistics of large-scale structure from the transition probability of Lagrangian trajectories

Lawrence Dam

We describe a probabilistic approach to gravitational clustering. Working purely in configuration space, we show that the two-point function can be formulated in terms of a transition probability describing the likely final position of a particle given its initial position. The transition probability is initially sharply peaked and then over time broadens out in a diffusion-like manner. The effect is more pronounced for widely-separated particles (e.g.\ at the baryon acoustic scale), leading to an effective suppression of the two-point function in what is normally referred to as infrared resummation.
Read the full abstract

Computation and Language

Agentic Detection of Online Conspiracies

Lior Biton and Oren Tsur

Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker's intent – the utterance's illocutionary force. We argue that this can be achieved through the use of relevant social contexts and propose an agentic framework, equipped with a set of tools supporting social queries. We demonstrate the benefits of our approach on a unique dataset of Hebrew tweets, covering 80%–90% of the public Hebrew tweets published over a four-year span (late 2018– early 2023), encompassing several election cycles as well as the COVID pandemic years and related vaccination campaigns. This extensive coverage can be used in recovering different social contexts. Evaluating our framework on a manually-annotated adversarial dataset, we find that context-aware workflows consistently outperform text-only classification and that the agentic framework performs better than other frameworks and settings, with significant gains over a non-agentic model exposed to the same contexts available to the agent. We further provide an analysis of the results, the errors and efficiency (token economy) tradeoffs. These findings support viewing the task of conspiracy detection as a socially embedded interpretation task, in which effective classification depends not only on access to contexts, but also on adaptive reasoning in which the agent uses tools on a per-case basis, asking only for evidence relevant to its current reasoning step.
Read the full abstract

Robotics

RAPID: Robot Agentic Programming from Demonstrations

Yuyao Liu, Jiayuan Mao, David Hsu et al. (+2 more)

Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration. The iterative agentic loop of code refinement requires several key ingredients: (i) a testable task specification, (ii) action primitives for robot execution, and (iii) an interactive environment for program execution and verification. RAPID infers all three from the demonstration automatically. To make the resulting program reusable beyond the demonstration setting, RAPID uses an object-centric relational program representation that focuses on the underlying structure of the demonstrated strategy rather than the specific motion per se: it expresses the action primitives as trajectory-optimization programs that realize object-level motion effects, while composing them through relational constraints that capture scene-specific geometry at run time. We evaluated RAPID in simulation on eight challenging contact-rich nonprehensile manipulation tasks as well as general prehensile manipulation tasks in the LIBERO-Pro benchmark. We also successfully deployed it on a real Franka arm and evaluated on all eight nonprehensile tasks. In all experiments, RAPID demonstrated strong performance, with generalization over object pose, shape, material, and environment. Website: https://yuyaoliu.me/projects/rapid.
Read the full abstract

All authors Yuyao Liu, Jiayuan Mao, David Hsu, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Quantum Physics

Deep thermalization and Hilbert space ergodicity

Daniel K. Mark, Manuel Endres, Matteo Ippoliti et al. (+2 more)

Recent advances in quantum simulation have enabled the discovery of novel forms of universality in quantum many-body dynamics. In this Review, we discuss deep thermalization and Hilbert space ergodicity–two recently discovered phenomena characterized by the emergence of distributions of quantum states that are "maximally random" in a precise sense. They offer a new perspective on how irreversible statistical mechanics arises from reversible unitary quantum dynamics, going beyond conventional theories of quantum thermalization and equilibration. We review a unifying framework, rooted in quantum information theory and principles of maximum entropy, that explains the different forms of ergodicity which arise under various physical constraints. We discuss open questions and active research directions, including generalizations beyond ensembles of pure quantum states, phase transitions in deep thermalization tied to deeper forms of ergodicity-breaking, connections to broader topics in quantum thermalization and ergodicity, and applications in quantum information science, such as for benchmarking or tomography.
Read the full abstract

All authors Daniel K. Mark, Manuel Endres, Matteo Ippoliti, Wen Wei Ho, Soonwon Choi

Robotics

Rolling-WAM: World Action Models with Rolling Imagination

Yinghua Zhou, Junjie Ye, Yiqi Zhao et al. (+8 more)

World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness. We present Rolling-WAM, a formulation that distributes joint denoising across successive replanning cycles. Our method maintains a sliding window of video-action chunks at staggered noise levels. At each step, a rolling noise schedule fully denoises the imminent action chunk for execution, while partially refining farther-future chunks. As the window advances with new camera observations, the retained future chunks continue their denoising process. This distributes the computational cost over time while carrying an evolving visual-action context across chunk boundaries. Evaluations on LIBERO, RoboTwin, and a real-world Unitree G1 humanoid show that Rolling-WAM achieves competitive manipulation performance. By removing the need to denoise the entire prediction horizon from scratch, it delivers a 4.5x steady-state replanning speedup over standard joint WAMs.
Read the full abstract

All authors Yinghua Zhou, Junjie Ye, Yiqi Zhao, Hao Dong, Celina Shiyu Wang, Ruohai Ge, Tingyi Yang, Basile Van Hoorick, Gaurav Sukhatme, Vitor Guizilini, Yue Wang

Quantum Physics

The Moreau-Yosida approximation of the Entanglement of Formation: basic properties and accuracy estimates

M.E.Shirokov

We describe a family of convex uniformly continuous functions $E^\lambda_F$, $\lambda>0$, on the set of states of a bipartite quantum system (consisting of finite-dimensional or infinite-dimensional subsystems), which monotonically increase and converge pointwise to the Entanglement of Formation (EoF) as $\lambda\to0$. These functions are "nonselective" entanglement monotones defined in a way close to the construction of the Moreau-Yosida regularization (the Moreau envelope) of a convex function on a convex set used in the modern convex analysis. So, we call the functions $E^\lambda_F$ the Moreau-Yosida approximations of the EoF and describe their equivalent definitions and basic properties. The semicontinuity bounds for the EoF (presented in [Lob.J.Math., 46(6), 2632-2658]) allow us to obtain easily computable upper bounds on the difference $E_F(\rho)-E^\lambda_F(\rho)$ for a given state $\rho$. These bounds give easily computable bounds on the rate of uniform convergence of the function $E^\lambda_F$ to the EoF as $\lambda\to0^+$ on the sets of states with bounded rank/energy of one of the marginal states. We also discuss sufficient conditions for the coincidence of $E_F(\rho)$ and $E^\lambda_F(\rho)$ at a given state $\rho$ for all $\lambda$ small enough and consider several classes of states for which such coincidence takes place. The conjectured selective LOCC-monotonicity of the functions $E^\lambda_F$ and a possible way to prove it are briefly discussed. Secondary parts of the article are written with the help of ChatGPT-5.6.
Read the full abstract

Computer Vision

Towards Practical Compression of 3D Gaussian Splatting

Pengpeng Yu, Yueru Chen, Fei Song et al. (+4 more)

3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over irregular 3D representations, increasing the complexity of training and coding. Meanwhile, floating-point context inference can introduce numerical inconsistencies across platforms, causing entropy-decoding failures. To address these practical challenges, we propose COSA-GS, which constructs context without spatial aggregation through anchor-wise causal factorization. Specifically, we use geometry context derived from each anchor's coordinates to model a compact learnable anchor latent. The anchor latent is then fused with the geometry context to form an anchor context for attribute coding. The resulting context model features a simple architecture composed solely of linear transformations and activations. We train COSA-GS using rate–distortion optimization with adaptive Gaussian pruning. Further, we develop quantization-aware training and integer inference for the context model to achieve bit-exact consistency of entropy-decoded symbols across platforms. Experiments demonstrate that COSA-GS achieves state-of-the-art compression performance while retaining fast and consistent cross-platform decoding, providing a simple yet effective framework for practical 3DGS compression. Code is available at https://github.com/pengpeng-yu/COSA-GS.
Read the full abstract

All authors Pengpeng Yu, Yueru Chen, Fei Song, Tai Qin, Qi Zhang, Jing Wang, Yulan Guo

Statistical Methodology

A Proxy-likelihood Estimator for Multivariate Extremes Models with Intractable Likelihoods

Troy P. Wixson and Daniel Cooley

Many multivariate extremes models have intractable likelihoods requiring practitioners to use alternative fitting methods. The tail pairwise dependence is a summary measure of the dependence in the tail of any multivariate regular variation model. We develop an objective function for model fitting that relies on the tail pairwise dependence as the link between our desired model (that does not have a likelihood) and a proxy model (that has a likelihood). We employ the bivariate Hüsler-Reiss distribution as the proxy model and show that there is a one-to-one relationship between the dependence parameter and the tail pairwise dependence value. Our proxy-likelihood estimator is fully developed for the transformed linear extremes time series (TLETS) models of Mhatre and Cooley (2024) and is applied to the wildfire weather data of Wixson and Cooley (2023). Simulations demonstrate that the proxy-likelihood is a competitive TPD estimator, is better at fitting TLETS models than existing methods, and is amenable to likelihood-based model selection techniques. Our estimator has smaller bias when tail dependence is weak than existing estimators reducing the need for bias adjustments. Without these adjustments, we note an increase in the tail dependence in weather-related wildfire risk between past and present climates.
Read the full abstract

Computation and Language

JevOut: Natural Context Can Flip Decision Models

Zixiang Xu, Zirui Song, Chiyu Zhang et al. (+4 more)

An ordinary-looking background detail can turn a correct model decision into a confident mistake. We demonstrate this fragility in four decision systems, including Jev, across seven datasets covering knowledge, reasoning, and tool routing. Within 64 accepted target evaluations per decision, we uncover short context additions that redirect 61.4%-73.2% of each system's initially correct decisions toward a wrong option fixed in advance. The additions supply background or procedural information rather than explicit answer-selection instructions, leaving the original question and choices intact. We construct them through probability-guided context optimization, which uses shifts in the option distribution to refine surrounding text under naturalness and answer-preservation constraints. Redirection affects initially confident decisions, often produces high-confidence wrong choices, and transfers across models. In blinded human evaluation, 91.6% of 250 sampled successful contexts are judged natural, answer-preserving, and free of decisive answer-changing evidence by a majority of three independent annotators. These findings expose a weakness in current decision models: context that looks entirely compatible with an input can redirect the choices that agents, routers, and evaluators rely on.
Read the full abstract

All authors Zixiang Xu, Zirui Song, Chiyu Zhang, Xiuying Chen, Xi Liu, Xiyang Hu, Yue Zhao

Algebraic Geometry

A Peterson program for general Schubert varieties and mirror symmetry

Changzheng Li, Konstanze Rietsch and Mingzhi Yang

We initiate a `Peterson program' that seeks to extend Dale Peterson's theory for the quantum cohomology of flag varieties to arbitrary Schubert varieties, and that incorporates a mirror-symmetric approach. In this first paper we introduce a generalisation of the (localised) Peterson variety associated to an arbitrary Schubert variety $\check X_{w,P}$ inside a partial flag variety $\check G/\check P$. This generalisation is possibly a non-reduced affine scheme that lies in the Langlands dual full flag variety $G/B$. Additionally, we construct a Lie-theoretic `superpotential' associated to $\check X_{w,P}$, generalising earlier ones for the flag varieties $\check G/\check P$ from [Rie08], and we show that its relative critical point locus recovers the generalised Peterson variety. We make the key conjecture that for smooth Fano Schubert varieties the coordinate ring of the generalised Peterson variety recovers the quantum cohomology ring of $\check X_{w,P}$ localised at the quantum parameters. For smooth Fano Schubert varieties $X_{w,B}$ in $\check G/\check B$ we furthermore map our generalised Peterson variety to the cotangent bundle $\mathcal T^*(T)$ of the maximal torus $T$ of $G$ and construct a partial compactification that we conjecture models the full quantum cohomology ring of $\check X_{w,B}$, in analogy with the Givental-Kim presentation of $QH^*(\check G/\check B)$ via the degenerate leaf of the Kostant Toda lattice of $\check G$. These conjectures are verified for all smooth Schubert divisors in the complete type $A$ flag variety, and we show that our superpotential agrees with that introduced for Grassmannian Schubert varieties by Rietsch and Williams, after a suitable isomorphism of the domains.
Read the full abstract

High Energy Physics (Theory)

The Flavor of Non-Abelian Orbifolds

Miguel Hernandez-Segura, Xiang-Gan Liu, Ricardo Perez-Martinez et al. (+1 more)

In string model building, flavor symmetries can arise from the geometric features of the compactification space. In the case of heterotic orbifold models, modular and traditional flavor symmetries can be related to both geometric and algebraic properties of the orbifold space group. Our goal is to study the origin of moduli-independent discrete flavor symmetries in symmetric heterotic orbifolds with non-Abelian point groups. We develop various methods based on the Abelianization and the geometric automorphisms of the orbifold space group to determine these symmetries and their associated charges for matter fields. Notably, our methods were applied to all 331 non-Abelian affine geometries, including roto-translations, suitable for delivering N=1 supersymmetric heterotic compactifications in four dimensions. This provides additional motivation to pursue the derivation of new types of phenomenologically viable models from string theory.
Read the full abstract

All authors Miguel Hernandez-Segura, Xiang-Gan Liu, Ricardo Perez-Martinez, Saul Ramos-Sanchez

High Energy Physics (Theory)

Emergent Scaling Symmetry and Universal Interior Criticality of Scalarized Black Holes

Ze Sun, Fu-Guo Yang, Rong-Gen Cai et al. (+2 more)

The interiors of spontaneously scalarized charged black holes exhibit a tower of universality classes, including the previously reported $1/2$ scaling. We show that their near-critical collapse is governed by an emergent scaling symmetry at the would-be Cauchy horizon. This symmetry renders the interior dynamics autonomous, model-independent, and exactly integrable, realizing first-kind self-similarity rather than Choptuik-type second-kind criticality. It also yields a “seesaw amplification”: an arbitrarily small exterior hair produces an arbitrarily large interior response. The amplification obeys an amplitude identity relating three independently measurable quantities. The singularity exponent equals the critical exponent of the exterior scalar charge. These relations connect the exterior phase transition to the approach to the interior singularity through the scaling dynamics near the would-be Cauchy horizon.
Read the full abstract

All authors Ze Sun, Fu-Guo Yang, Rong-Gen Cai, Li Li, Run-Qiu Yang

Probability

Multiple Stopping Options on a Geometric Random Walk

Katsunori Ano

This article develops a finite-horizon multiple-stopping framework and applies it to three American-style contracts on a geometric random walk in a Cox–Ross–Rubinstein market: an American put, a Russian option, and a floating-strike geometric-average Asian put. The general problem is represented by recursively defined Snell envelopes, with unused exercise rights encoded by a cemetery time; this yields an ordered optimal exercise vector without requiring all rights to be exercised. For the American put, a median representation of successive marginal values yields diminishing marginal values, nested exercise regions, and monotone exercise thresholds without relying on convexity of the marginal value. After suitable state reductions, analogous marginal-value arguments give threshold-type optimal exercise rules for the Russian and geometric-average Asian options. Independent random maturity is also incorporated, and its effect on the corresponding stopping regions is identified.
Read the full abstract

Computation and Language

SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data

Wenhao Li, Zhibin Wu, Chong Xiao et al. (+1 more)

Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment. It mainly consists of Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA). Specifically, CSR first adaptively extracts visual and acoustic representations by corresponding adapters to form a unified multimodal prefix with language in the frozen LLM embedding space. It then iteratively produces continuous discriminative semantic states through a token-efficient latent refinement process without decoding explicit text. Next, CSA simultaneously aligns the refined semantics with all modalities by enhancing the dominant spectral component of their kernel Gram matrix. This captures global nonlinear dependencies among all representations without relying on a predefined anchor modality. In addition, an instance-level spectral separation constraint preserves cross-sample discriminability and mitigates representation collapse. Extensive experiments on SIMS, MOSI, and MOSEI benchmarks demonstrate that SemMSA achieves state-of-the-art performance.
Read the full abstract

All authors Wenhao Li, Zhibin Wu, Chong Xiao, Qiangchang Wang

Optimization and Control

Proximity operator of the weighted squared $\ell_{2,\infty}$ norm with applications

Sergio López-Rivera

In this paper, we derive a closed-form for the proximity operator of the weighted squared $\ell_{2,\infty}$ norm. Moreover, we derive a closed-form for the proximity operator of a weakly convex version of this function. The proof involves using known results for compute the proximity operator of a supremum function, which requires finding a solution of an auxiliary problem. We find the explicit solution of this auxiliary problem by finding the KKT multipliers and the critical point associated to the first-order optimality conditions. Additionally, we present applications to denoising problems and weighted max-min dispersion problems.
Read the full abstract

Algebraic Geometry

Frobenius lifts and point counting for smooth curves

Amnon Besser, Rob de Jeu, Pengju Guan et al. (+1 more)

We describe an algorithm for computing the zeta function of a proper, smooth curve over a finite field $k$ of characteristic $p$, when the curve is given together with some auxiliary data, including a lift $C$ to the valuation ring in a finite extension of $\Q_p$. The algorithm is denominator-free if the ramification is at most $p$. Our method computes the matrix of the action of a semilinear Frobenius on the first de Rham cohomology group of the curve by means of Poincaré duality, using cup products that can be computed from local expansions of a globally defined lift of Frobenius. Its complexity is softly cubic in the field degree for (general) smooth, planar curve, for which we work out our general estimates in more detail. We make explicit how to compute a suitable basis of the first de Rham cohomology group of $C$, base on 1-forms with `locally integrable polar parts', in both the general case and when the curve is smooth planar. We show the crystalline Frobenius preserves the first de Rham cohomology group of $C$ if the ramification is at most $p$, improving upon known results. In an appendix we prove a well-known formula for the cup product, and a formula by Serre, on the first de Rham cohomology group for a curve in characteristic zero, for which no reference seems to exist.
Read the full abstract

All authors Amnon Besser, Rob de Jeu, Pengju Guan, Muxi Li

Metric Geometry

Moiré Pattern of Rotated Sierpiński Gaskets

Ziyu Neroli

We study the moiré pattern formed by the intersection of a planar Sierpiński gasket and a copy rotated about its centre. At every resonant angle, we prove that the intersection is of finite type and compute its Hausdorff and Minkowski dimensions from an explicit finite matrix; for Lebesgue-almost every angle, we bound its upper Minkowski dimension by $\overline{\dim}_{\mathrm B}(\mathcal S_\theta)\le 2\dim_{\mathrm H}(S)-2$. Finally, we conjecture that both dimensions equal this bound at every non-resonant angle. Sections 2 and 3 are formalised in Lean.
Read the full abstract

Computer Vision

OmniFabric: Coherent UV Space Texture Synthesis for 3D Garment Reconstruction

Ding-Jiun Huang, Yuanhao Wang, Cheng Zhang et al. (+4 more)

Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generative models have significantly advanced 3D geometry reconstruction, synthesizing high-quality textures remains a bottleneck. Existing methods often bake environmental illumination and shadows directly into the texture map, or they fail to maintain global structural coherence, making the resulting assets unusable for physical simulation and relighting. In this work, we introduce OmniFabric, a novel approach that synthesizes globally coherent texture maps directly within the 2D sewing pattern space. Given a single reference image, our pipeline utilizes an estimated 3D mesh and generative priors of powerful Vision-Language Models (VLM) to establish a complete but coarse texture initialization across the unwrapped sewing patterns. We then leverage a specialized diffusion transformer, trained via an automated synthetic data engine and conditioned on 3D positional features, to refine this initialization directly in the canonical UV domain. This effectively removes distortion and baked-in artifacts to extract a clean and normalized texture map that preserves the original garment design. Extensive experiments demonstrate that OmniFabric significantly outperforms state-of-the-art baselines, yielding photorealistic 3D garments with high-quality textures.
Read the full abstract

All authors Ding-Jiun Huang, Yuanhao Wang, Cheng Zhang, Hugo Bertiche, Alexandru-Eugen Ichim, Thabo Beeler, Fernando De la Torre

Robotics

Coding Agents for Generalized Task and Motion Planning Problems

Matteo Merler, Bowen Li, Josh Roy et al. (+4 more)

Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances. However, existing methods require substantial TAMP-specific engineering. We investigate whether coding agents can automate this process by synthesizing programs that generalize across instances. Given a task description and simulator access, each agent chooses how to interact with the environment while developing a program within a fixed synthesis budget. The program is then frozen and evaluated on unseen instances. We evaluate Claude Code (Opus 5) and Codex (GPT-5.6 Sol and GPT-6 Astra) on 28 simulated environments from KinDER and PDDLStream, with object counts beyond those evaluated in the original benchmark. Across all program synthesis methods, we evaluate 980 generated programs on 100 held-out instances each, 98,000 evaluation episodes in total. Overall, we find that coding agents are surprisingly effective at generalized TAMP: all three agent configurations outperform hand-engineered planners, one-shot generation, and an LLM-based generalized planning baseline in mean success (56% to 95% versus 47% for the planners, on the 16 environments where a planner is available). As object counts grow, the agents' programs maintain higher success than the planner, using an order of magnitude less computation per instance on average. Logs show agents using interaction to calibrate physical models, test edge cases, and refine strategies. We release all code, including the full prompts given to the agents. These findings suggest that coding agents are a strong baseline for generalized TAMP.
Read the full abstract

All authors Matteo Merler, Bowen Li, Josh Roy, Yichao Liang, Qianwei Wang, Yixuan Huang, Tom Silver

Astrophysics of Galaxies

Breaking the Blend: A multi-tracer kinematic decomposition method for IFS data applied to disentangling the AGN outflow and circumnuclear ring in NGC 5728 with JWST

Oscar Veenema, Niranjan Thatte, Dimitra Rigopoulou et al. (+26 more)

Integral field spectroscopy (IFS) of the central kiloparsecs of active galactic nuclei (AGN) reveals a mixture of spatially coincident emission from star-formation and AGN feedback exciting the interstellar medium. Disentangling these components remains a challenge, as most spectral tracers are affected by both processes, limiting robust interpretation of kinematics and energetics. We present a new framework for decomposing IFS data into distinct components on a spaxel-by-spaxel basis using a multi-tracer, stacked kinematics approach. This method combines kinematic modelling with imposed flux decomposition per spaxel, quantifying the contribution of each component across the field of view. We apply this method to JWST IFS observations of the Seyfert galaxy NGC 5728 from the Galaxy Activity, Torus, and Outflow Survey (GATOS), analysing twelve mid-infrared fine-structure lines (4.49 < $\lambda$ < 25.89 $\mu$m, 7.9 < IP < 126.2 eV). We find that the circumnuclear emission can be decomposed into two dominant components: a star-forming ring and an AGN-driven biconical outflow. Our method separates these structures and recovers their detailed spatial morphology. This framework provides a general and scalable method for physically motivated component separation in IFS data, applicable across many wavelength ranges and targets, enabling reliable interpretation of complex emission line structures (or morphologies) in active galaxies and beyond.
Read the full abstract

All authors Oscar Veenema, Niranjan Thatte, Dimitra Rigopoulou, Richard I. Davies, Miguel Pereira-Santaella, Ismael García-Bernete, Almudena Alonso-Herrero, Anelise Audibert, Enrica Bellocchi, Andrew J. Bunker, Françoise Combes, Tanio Díaz Santos, Fergus R. Donnan, Donaji Esparza-Arredondo, Federico Esposito, Santiago García-Burillo, Begoña García-Lorenzo, Omaira Gonzalez Martin, Laura Hermosa Muñoz, Erin K. S. Hicks, Sebastian F. Hönig, Masatoshi Imanishi, Alvaro Labiano, Nancy A. Levenson, Enrique Lopez-Rodriguez, Cristina Ramos Almeida, Claudio Ricci, Rogemar A. Riffel, Marko Stalevski

Analysis of PDEs

Hydrodynamic limits for the Boltzmann-Fermi-Dirac equation: Acoustic and Stokes-Fourier

Paulo Sampaio

We establish the acoustic and Stokes-Fourier hydrodynamic limits for the Boltzmann-Fermi-Dirac (BFD) equation on the torus of dimension 2 or more. For each limit, we consider fluctuations of appropriately scaled solutions of the BFD equation around a constant background Fermi-Dirac distribution. By adapting the weak solution framework developed by Bardos, Golse and Levermore for the classical Boltzmann equation to handle the cubic nonlinearity of the BFD collision operator, we show that these fluctuations converge weakly in time to a unique limit governed by the respective hydrodynamic equation.
Read the full abstract

Solar and Stellar Astrophysics

Disc-mediated angular momentum transport resolves the mass-gain problem in rotating binaries

Sebastian Ljung, Avishai Gilkis and Sandro Tacchella

A large fraction of stars interact with a close companion during their lifetime, in which the transfer of mass and angular momentum shapes their evolution and final fate. Standard rotationally limited accretion models predict that accretors reach critical rotation after gaining only a small fraction of their mass, severely suppressing further accretion. This is in tension with observations of post-interaction systems that require substantial mass gain. We introduce a disc-mediated angular momentum transport prescription for mass-transfer onto stellar companions. This is based on a novel perturbative, analytic star-disc boundary model that allows for continued mass inflow as the accretor approaches critical rotation. Furthermore, the model reproduces previous numerical results in which perturbations to super-critical rotation result in negative torques exerted by the disc, extracting excess angular momentum while allowing continued mass inflow. We implement this mechanism in detailed binary evolution calculations with differential rotation using MESA, and compute grids spanning primary mass, orbital period, and mass ratio. Whereas rotationally limited models predict $\beta_{\rm eff}\lesssim 0.1$, the disc model yields sustained mass inflow near critical rotation, with effective mass-transfer efficiencies of $\beta_{\rm eff}\sim 0.4$-$1$ across much of the parameter space. The resulting accretor properties are broadly consistent with observed post-interaction sdOB+Be binaries. Disc-mediated angular momentum transport may therefore represent a key missing ingredient in standard binary evolution models, with important implications for rapidly rotating stars and compact-object progenitors.
Read the full abstract